Large-scale identification of secreted and membrane-associated gene products using DNA microarrays

10.1038/75603 ◽  
2000 ◽  
Vol 25 (1) ◽  
pp. 58-62 ◽  
Author(s):  
Maximilian Diehn ◽  
Michael B. Eisen ◽  
David Botstein ◽  
Patrick O. Brown
Keyword(s):  
Large Scale ◽  
Dna Microarrays ◽  
Gene Products

Linking Interactome to Disease

2013 ◽  
pp. 151-172
Author(s):  
Maxime Garcia ◽  
Olivier Stahl ◽  
Pascal Finetti ◽  
Daniel Birnbaum ◽  
François Bertucci ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Large Scale ◽  
Dna Microarrays ◽  
Protein Interaction Data ◽  
Human Interactome ◽  
Protein Protein Interaction ◽  
Metastatic Relapse ◽  
High Throughput Gene Expression ◽  
Data Topology

The introduction of high-throughput gene expression profiling technologies (DNA microarrays) in molecular biology and their expected applications to the clinic have allowed the design of predictive signatures linked to a particular clinical condition or patient outcome in a given clinical setting. However, it has been shown that such signatures are prone to several problems: (i) they are heavily unstable and linked to the set of patients chosen for training; (ii) data topology is problematic with regard to the data dimensionality (too many variables for too few samples); (iii) diseases such as cancer are provoked by subtle misregulations which cannot be readily detected by current analysis methods. To find a predictive signature generalizable for multiple datasets, a strategy of superimposition of a large scale of protein-protein interaction data (human interactome) was devised over several gene expression datasets (a total of 2,464 breast cancer tumors were integrated), to find discriminative regions in the interactome (subnetworks) predicting metastatic relapse in breast cancer. This method, Interactome-Transcriptome Integration (ITI), was applied to several breast cancer DNA microarray datasets and allowed the extraction of a signature constituted by 119 subnetworks. All subnetworks have been stored in a relational database and linked to Gene Ontology and NCBI EntrezGene annotation databases for analysis. Exploration of annotations has shown that this set of subnetworks reflects several biological processes linked to cancer and is a good candidate for establishing a network-based signature for prediction of metastatic relapse in breast cancer.

scholarly journals An Approach to Inferring Transcriptional Regulation Among Genes From Large-Scale Expression Data

2003 ◽  
Vol 4 (1) ◽  
pp. 148-154 ◽  
Author(s):  
Javier Herrero ◽  
Ramón Díaz-Uriarte ◽  
Joaquín Dopazo
Keyword(s):  
Large Scale ◽  
Time Course ◽  
Dna Microarrays ◽  
Genetic Network ◽  
Genetic Networks ◽  
Gene Interactions ◽  
Expression Data ◽  
Dynamic Correlations ◽  
Common Time ◽  
Data Points

The use of DNA microarrays opens up the possibility of measuring the expression levels of thousands of genes simultaneously under different conditions. Time-course experiments allow researchers to study the dynamics of gene interactions. The inference of genetic networks from such measures can give important insights for the understanding of a variety of biological problems. Most of the existing methods for genetic network reconstruction require many experimental data points, or can only be applied to the reconstruction of small subnetworks. Here we present a method that reduces the dimensionality of the dataset and then extracts the significant dynamic correlations among genes. The method requires a number of points achievable in common time-course experiments.

scholarly journals Tom20 Mediates Localization of mRNAs to Mitochondria in a Translation-Dependent Manner

2009 ◽  
Vol 30 (1) ◽  
pp. 284-294 ◽  
Author(s):  
Erez Eliyahu ◽  
Lilach Pnueli ◽  
Daniel Melamed ◽  
Tanja Scherrer ◽  
André P. Gerber ◽  
...  
Keyword(s):  
Large Scale ◽  
Dna Microarrays ◽  
Protein Transport ◽  
Yeast Cells ◽  
Mitochondrial Proteins ◽  
Mitochondrial Outer Membrane ◽  
Dependent Manner ◽  
Mitochondrial Targeting ◽  
Double Knockout ◽  
Mitochondrial Fractions

ABSTRACT mRNAs encoding mitochondrial proteins are enriched in the vicinity of mitochondria, presumably to facilitate protein transport. A possible mechanism for enrichment may involve interaction of the translocase of the mitochondrial outer membrane (TOM) complex with the precursor protein while it is translated, thereby leading to association of polysomal mRNAs with mitochondria. To test this hypothesis, we isolated mitochondrial fractions from yeast cells lacking the major import receptor, Tom20, and compared their mRNA repertoire to that of wild-type cells by DNA microarrays. Most mRNAs encoding mitochondrial proteins were less associated with mitochondria, yet the extent of decrease varied among genes. Analysis of several mRNAs revealed that optimal association of Tom20 target mRNAs requires both translating ribosomes and features within the encoded mitochondrial targeting signal. Recently, Puf3p was implicated in the association of mRNAs with mitochondria through interaction with untranslated regions. We therefore constructed a tom20Δ puf3Δ double-knockout strain, which demonstrated growth defects under conditions where fully functional mitochondria are required. Mislocalization effects for few tested mRNAs appeared stronger in the double knockout than in the tom20Δ strain. Taken together, our data reveal a large-scale mRNA association mode that involves interaction of Tom20p with the translated mitochondrial targeting sequence and may be assisted by Puf3p.

scholarly journals Gene Info: Easy retrieval of gene product information on any website

2019 ◽  
Author(s):  
James D.R. Knight ◽  
Payman Samavarchi-Tehrani ◽  
Anne-Claude Gingras
Keyword(s):  
Gene Product ◽  
Large Scale ◽  
Product Information ◽  
Gene Products ◽  
High Quality ◽  
Constant Part

Viewing information about gene products is a constant part of the molecular biologist’s life. While there are many high quality and well-designed resources to fulfill this need, they require the user to navigate to these resources, execute a search, select the desired result and then view its information. This can be a repetitive, time-consuming and even disruptive process, for example when exploring the results of large scale genomics or proteomics screens or reading an online article.

scholarly journals RGBM: Regularized Gradient Boosting Machines for the Identification of Transcriptional Regulators of Discrete Glioma Subtypes

2017 ◽  
Author(s):  
Raghvendra Mall ◽  
Luigi Cerulo ◽  
Khalid Kunji ◽  
Halima Bensmail ◽  
Thais S. Sabedot ◽  
...  
Keyword(s):  
Large Scale ◽  
Dna Microarrays ◽  
Target Genes ◽  
Gene Knockout ◽  
Biological Activities ◽  
Expression Profiles ◽  
A Priori ◽  
Gradient Boosting ◽  
Gene Expressions ◽  
Cellular Phenotypes

AbstractThe transcription factors (TF) which regulate gene expressions are key determinants of cellular phenotypes. Reconstructing large-scale genome-wide networks which capture the influence of TFs on target genes are essential for understanding and accurate modelling of living cells. We propose RGBM: a gene regulatory network (GRN) inference algorithm, which can handle data from heterogeneous information sources including dynamic time-series, gene knockout, gene knockdown, DNA microarrays and RNA-Seq expression profiles. RGBM allows to use an a priori mechanistic of active biding network consisting of TFs and corresponding target genes. RGBM is evaluated on the DREAM challenge datasets where it surpasses the winners of the competitions and other established methods for two evaluation metrics by about 10-15%.We use RGBM to identify the main regulators of the molecular subtypes of brain tumors. Our analysis reveals the identity and corresponding biological activities of the master regulators driving transformation of the G-CIMP-high into the G-CIMP-low subtype of glioma and PA-like into LGm6-GBM, thus, providing a clue to the yet undetermined nature of the transcriptional events driving the evolution among these novel glioma subtypes.RGBM is available for download on CRAN at https://cran.rproject.org/web/packages/RGBM/index.html

Linking Interactome to Disease

2011 ◽  
pp. 406-427 ◽  
Author(s):  
Maxime Garcia ◽  
Olivier Stahl ◽  
Pascal Finetti ◽  
Daniel Birnbaum ◽  
François Bertucci ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Large Scale ◽  
Dna Microarrays ◽  
Protein Interaction Data ◽  
Human Interactome ◽  
Protein Protein Interaction ◽  
Metastatic Relapse ◽  
High Throughput Gene Expression ◽  
Data Topology

The introduction of high-throughput gene expression profiling technologies (DNA microarrays) in molecular biology and their expected applications to the clinic have allowed the design of predictive signatures linked to a particular clinical condition or patient outcome in a given clinical setting. However, it has been shown that such signatures are prone to several problems: (i) they are heavily unstable and linked to the set of patients chosen for training; (ii) data topology is problematic with regard to the data dimensionality (too many variables for too few samples); (iii) diseases such as cancer are provoked by subtle misregulations which cannot be readily detected by current analysis methods. To find a predictive signature generalizable for multiple datasets, a strategy of superimposition of a large scale of protein-protein interaction data (human interactome) was devised over several gene expression datasets (a total of 2,464 breast cancer tumors were integrated), to find discriminative regions in the interactome (subnetworks) predicting metastatic relapse in breast cancer. This method, Interactome-Transcriptome Integration (ITI), was applied to several breast cancer DNA microarray datasets and allowed the extraction of a signature constituted by 119 subnetworks. All subnetworks have been stored in a relational database and linked to Gene Ontology and NCBI EntrezGene annotation databases for analysis. Exploration of annotations has shown that this set of subnetworks reflects several biological processes linked to cancer and is a good candidate for establishing a network-based signature for prediction of metastatic relapse in breast cancer.

scholarly journals Mycobacterium tuberculosisFunctional Network Analysis by Global Subcellular Protein Profiling

2005 ◽  
Vol 16 (1) ◽  
pp. 396-404 ◽  
Author(s):  
Kwasi G. Mawuenyega ◽  
Christian V. Forst ◽  
Karen M. Dobos ◽  
John T. Belisle ◽  
Jin Chen ◽  
...  
Keyword(s):  
Cell Wall ◽  
Large Scale ◽  
Organizational Structures ◽  
Protein Profiling ◽  
Gene Products ◽  
Proteomics Data ◽  
Systematic Analysis ◽  
Its Gene ◽  
Proteome Profile ◽  
Related Proteins

Trends in increased tuberculosis infection and a fatality rate of ∼23% have necessitated the search for alternative biomarkers using newly developed postgenomic approaches. Here we provide a systematic analysis of Mycobacterium tuberculosis (Mtb) by directly profiling its gene products. This analysis combines high-throughput proteomics and computational approaches to elucidate the globally expressed complements of the three subcellular compartments (the cell wall, membrane, and cytosol) of Mtb. We report the identifications of 1044 proteins and their corresponding localizations in these compartments. Genome-based computational and metabolic pathways analyses were performed and integrated with proteomics data to reconstruct response networks. From the reconstructed response networks for fatty acid degradation and lipid biosynthesis pathways in Mtb, we identified proteins whose involvements in these pathways were not previously suspected. Furthermore, the subcellular localizations of these expressed proteins provide interesting insights into the compartmentalization of these pathways, which appear to traverse from cell wall to cytoplasm. Results of this large-scale subcellular proteome profile of Mtb have confirmed and validated the computational network hypothesis that functionally related proteins work together in larger organizational structures.

scholarly journals DNA Array-Based Transcriptional Analysis of Asporogenous, Nonsolventogenic Clostridium acetobutylicum Strains SKO1 and M5

2003 ◽  
Vol 185 (15) ◽  
pp. 4539-4547 ◽  
Author(s):  
Christopher A. Tomas ◽  
Keith V. Alsaker ◽  
Hendrik P. J. Bonarius ◽  
Wouter T. Hendriksen ◽  
He Yang ◽  
...  
Keyword(s):  
Electron Transport ◽  
Clostridium Acetobutylicum ◽  
Large Scale ◽  
Dna Microarrays ◽  
Expression Patterns ◽  
Central Carbon Metabolism ◽  
Transcriptional Analysis ◽  
Dna Arrays ◽  
Major Cluster ◽  
Solvent Formation

ABSTRACT The large-scale transcriptional program of two Clostridium acetobutylicum strains (SKO1 and M5) relative to that of the parent strain (wild type [WT]) was examined by using DNA microarrays. Glass DNA arrays containing a selected set of 1,019 genes (including all 178 pSOL1 genes) covering more than 25% of the whole genome were designed, constructed, and validated for data reliability. Strain SKO1, with an inactivated spo0A gene, displays an asporogenous, filamentous, and largely deficient solventogenic phenotype. SKO1 displays downregulation of all solvent formation genes, sigF, and carbohydrate metabolism genes (similar to genes expressed as part of the stationary-phase response in Bacillus subtilis) but also several electron transport genes. A major cluster of genes upregulated in SKO1 includes abrB, the genes from the major chemotaxis and motility operons, and glycosylation genes. Strain M5 displays an asporogenous and nonsolventogenic phenotype due to loss of the megaplasmid pSOL1, which contains all genes necessary for solvent formation. Therefore, M5 displays downregulation of all pSOL1 genes expressed in the WT. Notable among other genes expressed more highly in WT than in M5 were sigF, several two-component histidine kinases, spo0A, cheA, cheC, many stress response genes, fts family genes, DNA topoisomerase genes, and central-carbon metabolism genes. Genes expressed more highly in M5 include electron transport genes (but different from those downregulated in SKO1) and several motility and chemotaxis genes. Most of these expression patterns were consistent with phenotypic characteristics. Several of these expression patterns are new or different from what is known in B. subtilis and can be used to test a number of functional-genomic hypotheses.

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